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Car detection and damage segmentation in the real scene using a deep learning approach

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Abstract

Automatically detecting the outer car surface damage can considerably reduce the cost of processing premium assertion, and provide satisfaction for vehicle users. Since computer vision has a huge development among different research areas during recent years, the utilization of computer vision as a serious branch of science has also affected the object detection field. In this study, we develop an automated car and damage detection method based on a cascade Convolutional Neural Network (CNN). The presented method utilizes a pixel-based approach using two distinct CNNs, to determine the damage in outer region of a car among the achieved images. The experimental results indicate our proposed method obtains high performance in comparison to other state-of-the-art methods.

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The dataset used in this study can be obtained from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Mahboub Parhizkar & Majid Amirfakhrian

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  1. Mahboub Parhizkar
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  2. Majid Amirfakhrian
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The specific contributions made by each author is as follows: MP: Conceptualization, Methodology, Implementation, Writing-Original Draft, Writing—Review & Editing. MA: Conceptualization, Methodology, Implementation, Writing-Original Draft, Writing—Review & Editing.

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Correspondence to Majid Amirfakhrian.

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Parhizkar, M., Amirfakhrian, M. Car detection and damage segmentation in the real scene using a deep learning approach. Int J Intell Robot Appl 6, 231–245 (2022). https://doi.org/10.1007/s41315-022-00231-5

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